ArticleScientific reports2021
Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 29 citations in OpenAlex.
- Characterizing Age Effects on Wideband Absorbance in Normal-Hearing Children Via Statistical and Machine Learning Analyses.Journal of otology · 2026Article
- A novel approach to the follow up of children with otitis media with effusion: wideband absorbance findings.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants.Audiology research · 2025Article
- Article
- Wideband Absorbance Pattern and its Diagnostic Value in Adults with Middle Ear Effusions and Tympanic Membrane Perforation.The journal of international advanced otology · 2024Article
- Survey of Wideband Acoustic Immittance Use by Clinical Audiologists.American journal of audiology · 2024Article
- Multi-modal deep learning for joint prediction of otitis media and diagnostic difficulty.Laryngoscope investigative otolaryngology · 2024Article
- Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Review
- Three-dimensional wideband absorbance immittance findings in young adults with large vestibular aqueduct syndrome.Laryngoscope investigative otolaryngology · 2023Article
- The Rise and Fall of Aural Acoustic Immittance Assessment Tools.Seminars in hearing · 2023Review
- Implementation of Wideband Acoustic Immittance in Clinical Practice: Relationships among Audiologic and Otologic Findings.Seminars in hearing · 2023Article
- Endorsing a Hidden Plasmonic Mode for Enhancement of LSPR Sensing Performance in Evolved Metal-insulator Geometry Using an Unsupervised Machine Learning Algorithm.ACS physical chemistry Au · 2022Article
Corrections and comments
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Authors and funding
9 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results. This study aimed to develop Machine Learning (ML) tools to identify the WAI absorbance characteristics across different frequency-pressure regions in the normal middle ear and ears with otitis media with effusion (OME) to enable diagnosis of middle ear conditions automatically. Data analysis included pre-processing of the WAI data, statistical analysis and classification model development, and key regions extraction from the 2D frequency-pressure WAI images. The experimental results show that ML tools appear to hold great potential for the automated diagnosis of middle ear diseases from WAI data. The identified key regions in the WAI provide guidance to practitioners to better understand and interpret WAI data and offer the prospect of quick and accurate diagnostic decisions.
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Registered trials
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